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http://hdl.handle.net/20.500.12128/7716
Tytuł: | Enhancing the Efficiency of a Decision Support System through the Clustering of Complex Rule-Based Knowledge Bases and Modification of the Inference Algorithm |
Autor: | Nowak-Brzezińska, Agnieszka |
Słowa kluczowe: | Decision Support System; rule-based knowledge |
Data wydania: | 2018 |
Źródło: | Complexity, Vol. 13 (2018), art. ID 2065491 |
Abstrakt: | Decision support systems founded on rule-based knowledge representation should be equipped with rule management
mechanisms. Effective exploration of new knowledge in every domain of human life requires new algorithms of knowledge
organization and a thorough search of the created data structures. In this work, the author introduces an optimization of both
the knowledge base structure and the inference algorithm. Hence, a new, hierarchically organized knowledge base structure is
proposed as it draws on the cluster analysis method and a new forward-chaining inference algorithm which searches only the
so-called representatives of rule clusters. Making use of the similarity approach, the algorithm tries to discover new facts (new
knowledge) from rules and facts already known. The author defines and analyses four various representative generation
methods for rule clusters. Experimental results contain the analysis of the impact of the proposed methods on the efficiency of a
decision support system with such knowledge representation. In order to do this, four representative generation methods and
various types of clustering parameters (similarity measure, clustering methods, etc.) were examined. As can be seen, the
proposed modification of both the structure of knowledge base and the inference algorithm has yielded satisfactory results. |
URI: | http://hdl.handle.net/20.500.12128/7716 |
DOI: | 10.1155/2018/2065491 |
ISSN: | 1099-0526 |
Pojawia się w kolekcji: | Artykuły (WNŚiT)
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